def GenerateProposals(self, blobs_in, blobs_out, anchors, spatial_scale):
"""Op for generating RPN porposals.
blobs_in:
- 'rpn_cls_probs': 4D tensor of shape (N, A, H, W), where N is the
number of minibatch images, A is the number of anchors per
locations, and (H, W) is the spatial size of the prediction grid.
Each value represents a "probability of object" rating in [0, 1].
- 'rpn_bbox_pred': 4D tensor of shape (N, 4 * A, H, W) of predicted
deltas for transformation anchor boxes into RPN proposals.
- 'im_info': 2D tensor of shape (N, 3) where the three columns encode
the input image's [height, width, scale]. Height and width are
for the input to the network, not the original image; scale is the
scale factor used to scale the original image to the network input
size.
blobs_out:
- 'rpn_rois': 2D tensor of shape (R, 5), for R RPN proposals where the
five columns encode [batch ind, x1, y1, x2, y2]. The boxes are
w.r.t. the network input, which is a *scaled* version of the
original image; these proposals must be scaled by 1 / scale (where
scale comes from im_info; see above) to transform it back to the
original input image coordinate system.
- 'rpn_roi_probs': 1D tensor of objectness probability scores
(extracted from rpn_cls_probs; see above).
"""
name = 'GenerateProposalsOp:' + ','.join([str(b) for b in blobs_in])
# spatial_scale passed to the Python op is only used in convert_pkl_to_pb
self.net.Python(
GenerateProposalsOp(anchors, spatial_scale, self.train).forward)(
blobs_in, blobs_out, name=name, spatial_scale=spatial_scale)
return blobs_out
def GenerateProposals(self, blobs_in, blobs_out, anchors, spatial_scale):
# blobs_in = ['rpn_cls_probs', 'rpn_bbox_pred', 'im_info']
# blobs_out = ['rpn_rois', 'rpn_roi_probs']
name = 'GenerateProposalsOp:' + ','.join([str(b) for b in blobs_in])
self.net.Python(
GenerateProposalsOp(anchors, spatial_scale,
self.train).forward)(blobs_in,
blobs_out,
name=name)
return blobs_out